RAG Project with Ollama & ChromaDB

A Retrieval-Augmented Generation (RAG) application that allows you to upload PDF documents and ask questions about their content using local AI models.

🚀 Features

• PDF Document Processing: Upload and process PDF files for question-answering
• Local AI Models: Uses Ollama for both embeddings and language generation
• Vector Search: ChromaDB for efficient document retrieval
• Interactive Web Interface: Streamlit-based user interface
• Privacy-First: All processing happens locally - no data sent to external APIs

🛠️ Tech Stack

• Frontend: Streamlit
• Language Model: Llama 3.2:1b (via Ollama)
• Embeddings: nomic-embed-text (via Ollama)
• Vector Database: ChromaDB
• Document Processing: LangChain
• Package Management: uv

📋 Prerequisites

Before running this application, make sure you have:

1. Python 3.12+ installed
2. Ollama installed and running
3. uv package manager installed

Installing Ollama

Download and install Ollama from ollama.ai

Installing uv

# On Windows (PowerShell)
curl -LsSf https://astral.sh/uv/install.ps1 | powershell

# On macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/Akash-Dutta07/RAG-Agent-Using-Ollama.git
cd RAG-Agent-Using-Ollama/project

2. Set Up Python Environment

# Initialize uv project (if not already done)
uv init

# Install dependencies
uv add -r requirements.txt

# Create lock file and sync
uv lock
uv sync

3. Download Required Ollama Models

# Download the language model
ollama pull llama3.2:1b

# Download the embedding model
ollama pull nomic-embed-text

4. Verify Models are Downloaded

ollama list

You should see both llama3.2:1b and nomic-embed-text in the list.

5. Run the Application

# Make sure you're in the project directory
streamlit run main.py

The application will open in your browser at http://localhost:8501

💻 Usage

1. Upload PDF: Click "Browse files" or drag and drop a PDF file
2. Process Document: Click the "Process" button to create embeddings
3. Ask Questions: Enter your questions in the text area and click "Get Answer"
4. View Context: Expand "Show Context" to see the source text used for the answer

📁 Project Structure

project/
├── main.py                 # Streamlit application
├── supporting_functions.py # RAG implementation
├── requirements.txt        # Python dependencies
├── pyproject.toml         # uv project configuration
└── README.md              # This file

🔧 Configuration

You can modify the models used by editing supporting_functions.py:

EMBEDDING_MODEL = "nomic-embed-text"  # Embedding model
LLM_MODEL = "llama3.2:1b"            # Language model

Available Ollama Models

• Language Models: llama3.2:1b, llama3.2:3b, qwen2.5:7b, etc.
• Embedding Models: nomic-embed-text, mxbai-embed-large, etc.

📚 Dependencies

Key dependencies include:

• streamlit: Web interface
• langchain: Document processing framework
• langchain-community: Community extensions
• langchain-ollama: Ollama integration
• langchain-text-splitters: Text chunking
• chromadb: Vector database
• pypdf: PDF processing
• ollama: Ollama client

🐛 Troubleshooting

Common Issues

1. Model not found error

   # Download the missing model
   ollama pull <model-name>

2. Ollama connection error

   # Check if Ollama is running
   ollama list
   
   # Start Ollama if not running
   ollama serve

3. Import errors

   # Reinstall dependencies
   uv sync --force

4. PDF processing errors

   • Ensure PDF is not corrupted
   • Check file size (limit: 200MB)
   • Try with a different PDF

⭐ If you found this project helpful, please give it a star on GitHub!